Evolutionary Trends in Multimorbidity Patterns of Chronic Diseases Among Middle-Aged and Older Adults in China: A Demographic Disparity Analysis.

Abstract

Multimorbidity is increasing, yet its longitudinal evolution remains poorly characterized. This study identified multimorbidity patterns, quantified their evolution and transitions, and assessed demographic and trajectory differences. Using 5 waves of data from the China Health and Retirement Longitudinal Study collected from 2011 to 2020 and analyzed in 2025, fuzzy c-means clustering of chronic diseases was applied among individuals with multimorbidity to identify multimorbidity patterns. Adjacent-wave transitions were summarized with matrices and flow diagrams, and pattern sequences were hierarchically clustered to define trajectory clusters. Discrete-time multistate models and multinomial logistic regression examined correlates of key transitions and cluster membership. Mortality differences were compared using chi-square tests; missing-data sensitivity analyses were performed. Multimorbidity patterns evolved from early predominance of the gastrointestinal-musculoskeletal, musculoskeletal-respiratory, and cardiometabolic-vascular risk patterns to more complex structures with greater cardiometabolic-vascular burden in later waves. Pattern evolution showed demographic heterogeneity across sex, age, residence, education, and marital status. Six trajectory clusters were identified, and cumulative mortality differed across both trajectory clusters and multimorbidity patterns (p<0.05 for both). Poor self-rated health was associated with higher risks of transitions from healthy to single condition and from single condition to multimorbidity; single condition to multimorbidity was also associated with older age, depressive symptoms, and body pain. Older age, body pain, depressive symptoms, and poor self-rated health were associated with higher RRRs of membership in nonhealthy trajectory clusters. Multimorbidity increased and became more complex over time, with demographic heterogeneity and stratifiable prognostic differences. Dynamic surveillance and risk-stratified integrated care may be prioritized for cardiometabolic-vascular high-risk domains and high-burden trajectories, alongside early, continuous management for vulnerable subgroups.